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Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 851–875 of 2226 papers

TitleStatusHype
Quasi Black-Box Variational Inference with Natural Gradients for Bayesian Learning—0
Flexible and Hierarchical Prior for Bayesian Nonnegative Matrix Factorization—0
Parameters identification for an inverse problem arising from a binary option using a Bayesian inference approach—0
RL with KL penalties is better viewed as Bayesian inference—0
DPER: Dynamic Programming for Exist-Random Stochastic SATCode0
Variational Inference for Bayesian Bridge Regression—0
Nonblind image deconvolution via leveraging model uncertainty in an untrained deep neural networkCode0
Marginal and Joint Cross-Entropies & Predictives for Online Bayesian Inference, Active Learning, and Active Sampling—0
DPO: Dynamic-Programming Optimization on Hybrid ConstraintsCode0
Intuitive and Efficient Human-robot Collaboration via Real-time Approximate Bayesian Inference—0
On the Convergence of the Shapley Value in Parametric Bayesian Learning GamesCode0
Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems—0
Addressing Census data problems in race imputation via fully Bayesian Improved Surname Geocoding and name supplements—0
Scalable Stochastic Parametric Verification with Stochastic Variational Smoothed Model Checking—0
Sequential Importance Sampling for Hybrid Model Bayesian Inference to Support Bioprocess Mechanism Learning and Robust Control—0
A Deep Learning Approach to Dst Index Prediction—0
Bézier Curve Gaussian Processes—0
TopWORDS-Seg: Simultaneous Text Segmentation and Word Discovery for Open-Domain Chinese Texts via Bayesian Inference—0
A Dataset-free Deep learning Method for Low-Dose CT Image Reconstruction—0
Approximating Permutations with Neural Network Components for Travelling Photographer Problem—0
Deep Ensemble as a Gaussian Process Approximate Posterior—0
Statistical applications of contrastive learning—0
Designing Perceptual Puzzles by Differentiating Probabilistic Programs—0
Zero-Shot Logit AdjustmentCode1
A Bayesian Approach To Graph Partitioning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1F-SWAAccuracy83.61—Unverified
2F-SWAGAccuracy80.93—Unverified